code: def test_espnet_model_translatotron(encoder):
      vocab_size = 5
      enc_out = 4
      encoder = encoder(
          20, output_size=enc_out, linear_units=4, num_blocks=2
      )
      asr_decoder = TransformerDecoder(
          vocab_size, enc_out, linear_units=4, num_blocks=2
      )
      st_decoder = TransformerDecoder(
          vocab_size, enc_out, linear_units=4, num_blocks=2
      )
      synthesizer = Translatotron(
          enc_out,
          15,
          embed_dim=4,
          adim=4,
          dunits=2,
          dlayers=2,
          prenet_layers=1,
          postnet_layers=1,
          postnet_chans=2,
      )
      asr_ctc = CTC(odim=vocab_size, encoder_output_size=enc_out)
      st_ctc = CTC(odim=vocab_size, encoder_output_size=enc_out)
  
      losses = {
          "src_attn": S2STAttentionLoss(vocab_size),
          "tgt_attn": S2STAttentionLoss(vocab_size),
          "synthesis": S2STTacotron2Loss(),
          "asr_ctc": S2STCTCLoss(),
          "st_ctc": S2STCTCLoss(),
      }
  
      model = ESPnetS2STModel(
          s2st_type="translatotron",
          frontend=None,
          src_normalize=None,
          tgt_normalize=None,
          tgt_feats_extract=None,
          specaug=None,
          encoder=encoder,
          preencoder=None,
          postencoder=None,
          aux_attention=None,
          unit_encoder=None,
          losses=losses,
          asr_decoder=asr_decoder,
          st_decoder=st_decoder,
          synthesizer=synthesizer,
          asr_ctc=asr_ctc,
          st_ctc=st_ctc,
          tgt_vocab_size=vocab_size,
          tgt_token_list=["<blank>", "<unk>", "a", "i", "<eos>"],
          src_vocab_size=vocab_size,
          src_token_list=["<blank>", "<unk>", "a", "i", "<eos>"],
          unit_vocab_size=None,
          unit_token_list=None,
      )
  
      inputs = dict(
          src_speech=torch.randn(2, 10, 20, requires_grad=True),
          src_speech_lengths=torch.tensor([10, 8], dtype=torch.long),
          src_text=torch.randint(2, 4, [2, 4], dtype=torch.long),
          src_text_lengths=torch.tensor([4, 3], dtype=torch.long),
          tgt_text=torch.randint(2, 4, [2, 4], dtype=torch.long),
          tgt_text_lengths=torch.tensor([4, 3], dtype=torch.long),
          tgt_speech=torch.randn(2, 10, 15, requires_grad=True),
          tgt_speech_lengths=torch.tensor([10, 8], dtype=torch.long),
      )
      loss, *_ = model(**inputs)
      loss.backward()
  
file_path: espnet/test/espnet2/s2st/test_s2st_espnet_model.py
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.s2st.aux_attention.multihead import MultiHeadAttention
  from espnet2.s2st.espnet_model import ESPnetS2STModel
  from espnet2.s2st.losses.attention_loss import S2STAttentionLoss
  from espnet2.s2st.losses.ctc_loss import S2STCTCLoss
  from espnet2.s2st.losses.guided_attention_loss import S2STGuidedAttentionLoss
  from espnet2.s2st.losses.tacotron_loss import S2STTacotron2Loss
  from espnet2.s2st.synthesizer.discrete_synthesizer import TransformerDiscreteSynthesizer
  from espnet2.s2st.synthesizer.translatotron import Translatotron
label_desc: Use image node_id 2 for calling a global function with example usage: test_espnet_model_translatotron(encoder) without return types